ScoAI: Toward OR-Native Decision Intelligence

Source abstract

Foundation models and AI agents are increasingly used to read operational requests, estimate uncertain quantities, and draft plans. In supply chains, these outputs feed decisions that must respect costs, stock and capacity limits, and business rules, and whose effects become visible only after delayed and often partial execution. This perspective, ScoAI = SC + OR + AI, argues that learned components should be designed around the decision they support. Supply-chain context defines the problem, operations research supplies objectives, constraints, and coordination, and AI supplies estimates and judgments that can be reused across tasks. We call a model or agent OR-native when these elements are explicit in its interface, and we refer to the coupling of learned components with executable operational relations as data--mechanism fusion. The coupling can fail in three places: when a judgment is turned into an action, when a locally good choice changes the cost of later stages, and when the executed action differs from the approved one. For each, we give a short analysis. Cost-sensitive thresholds and a decomposition of decision loss address the first; completion values and resource prices address the second; and a censoring argument shows how executed actions limit what operational logs can teach. Systems developed at JD.com illustrate these interfaces, and small constructed examples make every calculation reproducible. We close with decision records, an agent architecture, and evaluation protocols that compare reusable components with specialist workflows on decision quality, validity, coverage, and total cost.